How to Get the First Row of Each Group in a Pandas DataFrame?

Getting the First Row of Each Group in a Pandas DataFrame

In pandas, groupby operations allow for efficient data aggregation and manipulation across different categories. However, retrieving specific rows within each group can be a challenge. This article will demonstrate how to retrieve the first row of each group when grouping a pandas DataFrame.

Problem:

We have a DataFrame with two columns, "id" and "value." We want to group the DataFrame by "id," "value," and get the first row of each group.

Expected Outcome:

id	value
1	first
2	first
3	first
4	second
5	first
6	first
7	fourth

Solution:

To retrieve the first row of each group, we can use the .first() method. By passing "id" as the group key, .first() selects the first non-null element for each unique "id" group.

df.groupby('id').first()

This will produce the desired output, with the first row of each "id" group displayed.

Getting Identifier as Column:

If we need the identifier as a column, we can use .reset_index().

df.groupby('id').first().reset_index()

This yields:

id	value
1	first
2	first
3	first
4	second
5	first
6	first
7	fourth

Retrieving Multiple Rows:

To retrieve the first n rows of each group, we can use .head().

df.groupby('id').head(2).reset_index(drop=True)

This allows us to retrieve specified number of rows from the beginning of each group.

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